Key Engineering Materials Vol. 693

Abstract: Complex mass faults diagnosis of the pantograph type current collector was difficult. Based on the analysis of the structure, working principle and failure mode of the pantograph type current collector, fault tree of the pantograph was established. A lot of expert knowledge has been collected to support this diagnose process. Some serious problems such as ambiguity, uncertainty and inconsistency exist in the knowledge. Focused on the deficiencies, ontology modeling was proposed in this paper. StrOnto, FaultOnto and FTOnto were established to standardize the knowledge and to improve the efficiency of fault diagnosing. Finally, combined with the example of the pantograph type current collector of CRH2 EMU-train, the proposed algorithms proposed in this paper were proved reasonable and effective.

Abstract: ion Method for Displacement Response in the Sweep-Frequency Testing of Rotating Spindle Guo Yuzhu^{1, a*}, Chen Tianning^{1, b}, Wang Xiaopeng^{1, c} and Xu Yanfei^{1 1} State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an Jiaotong University, 710049 Xi’an, China ^aguoyuzhu@126.com, ^btnchen@mail.xjtu.edu.cn, ^cxpwang@mail.xjtu.edu.cn Keywords: Rotating spindle; Sweep-frequency measurement; Error separation. Abstract. In the measurement of motorized spindle dynamic performance, the non-contact sweep-frequency excitation is adopted to obtain the displacement response of spindle rotor under high speed rotating. Due to the shape error at measurement section, eccentric error and random vibration, the illusory signals is introduced to the practical measured signal, which makes it difficult to analyze the displacement response. To effectively separate the error, a new method combining template signal and slip filtering is presented. After analyzing the characteristics of displacement response and illusory signals in time-frequency domain, the template signal constructed by the non-superposing portions between response and error is employed to eliminate the error in superposing portions. And a band-pass filter which can slip in time-frequency domain is employed to eliminate the other error. A spindle of CNC milling machine is tested at 7000 r/min, and the analysis result shows that the displacement response can be abstracted successfully from the original signal measured by rotating spindle.

Abstract: This paper presents the fault mode and barb causality diagram of the pantograph of the emu, by settling and counting fault information of the pantograph. And fault analysis of the pantograph is used to establish the fault tree. The Petri net model of the pantograph is simplified on the base of the principle of duality and resorption by combining the modeling theory of Petri net and the fault tree. The minimum cut set is obtained by solving the above simplified model with incidence matrix. Compared to common solution of the fault tree, this integrated model efficiently simplifies computation of complicated fault tree. The method raised here saves the time of computation which increases the efficiency of analysis, and improves the accuracy of fault diagnosis.

Abstract: In this paper, a space vector PWM (SVPWM) control algorithm based on BP neural network is proposed to cope with the complex calculation required in SVPWM through analyzing SVPWM for three phase voltage fed inverter. This method uses the strong nonlinear approximation ability of the BP neural network to fit the eqivalent segment SVPWM modulated wave, modulate with the triangular carrier wave, and then get the control signals of the three phase voltage inverter. A simulation model for PMSM control system was developed by MATLAB/Simulink with the neural network Toolbox. The results show that the BP neural network based SVPWM algorithm makes the motor control system has a smaller current harmonics and torque ripple, and reduce the amount of computation in digital control system with strong guidance.

Abstract: The system of binocular vision sensor was used in the air-to-air close air target positioning in the paper. Due to the limitation of model itself, the measurement accuracy along the direction of optical axis is far lower than the accuracy of vertical direction. In order to improve the measurement accuracy of the optical axis, the paper put forward to using laser range sensor to cooperate with binocular vision sensor; Then the paper proposed adopts adaptive weighted fusion algorithm of multi-sensor information fusion to improve the utilization efficiency of multi-sensor information and to make the results accurately; Finally, the parameters of the system were calibration respectively and experiment is simulated, experimental results show that the position system is feasibility and effectiveness.

Abstract: Oil tubing is one of the most used equipment in oil extraction operations. An effective diagnosis system for it can provide multifarious benefits such as improved safety, efficient production and reduced costs for maintenance. In this paper, a support vector machine (SVM) based diagnosis system for oil tubing is studied and designed. SVM method has many advantages in solving the problem of small sample, pattern recognition of high dimensionality and nonlinear problems, which is fitable to the situation of oil tubing diagnosis. The SVM based diagnosis system for oil tubing is consisted of two parts: The hardware system part, including the detector and conditioning circuit board, and the software system part, including the SVM based analysis system. The detector is disposed on the wellhead and detects the leakage of magnetic field. The conditioning circuit board focuses on signal amplification and noise removal. The SVM based analysis system diagnoses the faults by the features of detected signal. An experiment platform is designed to certify the whole system and prove it perform well with a high diagnosis accuracy.

Abstract: Fault state is central to the achievement of equipment operation stability and security. On the basis of the analysis of the general process, basic characteristics and evolution of rolling bearing fault formation, according to the uncertainty of rolling bearing fault generation mechanism, highly nonlinear of fault evolution and diversity of fault modes, establishing a rolling bearing fault evolution model based on vibration time domain parameters.

Abstract: A novel scale-invariant feature transform (SIFT) algorithm is proposed for soccer target recognition application in a robot soccer game. First, the method of generating scale space is given, extreme points are detected. This gives the precise positioning of the extraction step and the SIFT feature points. Based on the gradient and direction of the feature point neighboring pixels, a description of the key points of the vector is generated. Finally, the matching method based on feature vectors is extracted from SIFT feature points and implemented on the image of the football in a soccer game. By employing the proposed SIFT algorithm for football and stadium key feature points extraction and matching, significant increase can be achieved in the robot soccer ability to identify and locate the football.

Abstract: The way of efficiently classifying the manual digging, machine excavation, vehicle passing and other pipeline security threats, is an imperative problem for optical fiber pipeline security warning system. To solve this problem, a security threats classification method based on optimized support vector machine is proposed. In this method, after feature extraction based on wavelet to the original vibration signal, the artificial bee colony algorithm is introduced to optimize the penalty factor and kernel parameter of support vector machine under specified fitness function, and the optimized support vector machine is used to classify the pipeline security threats. To testify the performance of the proposed method, the experiment based on UCI feature datasets and actual vibration signal are made. Comparing with the support vector machine optimized by other algorithms, higher classification accuracy and less time consumption is achieved by the proposed method. Therefore, the effectiveness and the engineering application value of this proposed method is testified.

Abstract: Fault diagnosis of train bearing is an important method to ensure the security of railway. In the paper, four time domain statistical analysis methods and resonance demodulation method as a frequency domain analysis method are introduced. A state inspection and fault diagnosis system of train axel box bearing is constructed and its efficiency is also tested by wheel set experiment.